Which one to choose to identify patterns of user activity: Sequence analysis or process mining?
I have the following user activity data that where for each user the activity type they were engaged are recorded along with the phase:
User | Phase | ActivityType | Date
321 1 A 12/20/2020 15:00
321 1 B 12/20/2020 16:00
321 2 A 12/21/2020 12:00
321 1 C 12/21/2020 13:00
321 3 B 12/22/2020 11:00
322 1 A 12/20/2020 15:00
322 1 A 12/20/2020 16:00
322 2 B 12/21/2020 12:00
322 1 C 12/21/2020 13:00
322 3 D 12/22/2020 11:00
For each user, I also have the satisfaction score about the application.
User | Satisfaction
321 90
321 60
What I want to see is if there are any emerging groups of users with a certain pattern of activities. Then, I want to compare the satisfaction scores across these groups to check if the specific pattern of activities yield higher satisfaction or not.
To perform this analysis, I identified two approaches: process mining (with PM4PY, python library), and sequence analysis (with TraMineR, an R library).
However I am not sure which one would fit better to my needs. I am a total beginner in both areas. Any insights to help me make a good decision here?
Topic sequential-pattern-mining python r data-mining
Category Data Science